The invention relates generally to image processing and image analysis. More specifically, the present techniques relate to analysis of tissue microarrays made from serial sections of a multiple-tissue sample block.
Tissue microarrray (TMA) technology has become the standard in large-scale immunohistochemistry (IHC), fluorescent in situ hybridization (FISH), and mRNA in situ hybridization (RNA-ISH) studies for protein, DNA and RNA expression. To prepare the TMA slides, a tissue core is typically obtained from the patient tissue and inserted in a paraffin recipient block. The resulting recipient block typically has hundreds of tissue cores from multiple patients. This block may then be cut into sections that may be placed on glass slides for examination and imaging. In each of the serial sections, corresponding tissue cores belong to the same patient, and it is advantageous to be able to relate each patient core in one TMA slide to the corresponding one belonging to the same patient in another slide.
However, alignment of TMAs made from serial sections of the block is difficult. For example, the grid on the block is often not rectilinear, preventing simple alignment of multiple slides to line up individual samples. The misalignment is even more pronounced when each tissue core is imaged individually (in contrast to whole slide imaging where the entire slide is scanned into a single image). For example, in high-resolution fluorescent microscopy on TMAs, the microscope is manually or automatically moved to each core and a suitable region of the tissue core imaged. This makes the resulting grid even less rectilinear since the portion of the tissue imaged may vary slightly from core to core. In addition, it is common for a few tissue cores to fall off from the slides, and missing cores from one TMA slide are often different from those missing from another. However, in order to compare results between TMA slides made from the same block, it is advantageous to be able to link samples from the same patient.
Certain techniques focus on reducing the incidence of samples falling off of individual TMA slides. However, even if one tissue spot falls off, the correspondence between serial sections is lost and should be re-established before individual samples on the TMAs may be compared.
Other techniques for identifying samples on a TMA slide focus on one TMA and not on the use of multiple TMAs from adjacent sections. For example, a deformable mesh grid approach involves the user defining the number of rows and columns on the TMA and the software automatically generating a deformable mesh grid with the specified dimensions. The grid may be adjusted on a whole-slide image to match the layout of the spots on the TMA in a semi-automatic way, i.e., requiring some user intervention to adjust the grid. The elements of the grid can also be associated with TMA numbers to facilitate correlation with clinical information. This semi-automatic approach requires a whole slide image to be available and is not useful for imaging systems that do not produce whole slide images. Further, this system is not designed to correlate TMAs made from serial sections, and the loss of a number of spots on different slides may affect the deformable grid assignment. Other approaches, such as image-based approaches, analyze a whole slide image to identify the location of tissue cores on the slide and automatically generate a grid from the result of the analysis. These approaches require the tissue spots on the slide to confirm to a linear grid structure and do not correlate patient samples on one TMA to those on another TMA, or to relevant clinical information about the tissue spots.